- Design, develop, and deploy AI and machine learning solutions tailored to retail challenges such as personalized product recommendations, dynamic pricing, and demand forecasting.
- Collaborate with data scientists, product managers, engineers, and retail analysts to develop AI-driven features that improve customer experience and operational efficiency.
- Build and manage data pipelines that support large-scale training and inference workloads using structured and semi-structured retail data.
- Develop and optimize deep learning models using TensorFlow or PyTorch for applications like visual product search, customer segmentation, and chatbot automation.
- Integrate AI models into customer-facing platforms (e.g., mobile apps, websites) and backend retail systems (e.g., inventory management, logistics).
- Monitor model performance post-deployment and implement continuous improvement strategies based on business KPIs and real-time data.
- Contribute to model governance, testing, and documentation to ensure models are fair, explainable, and secure.
- Stay informed about AI trends in the retail and e-commerce industry to help the team stay competitive and innovative.
Required Skills & Experience:
AI/ML Expertise:
- Minimum of 3 years of experience in developing and deploying machine learning models in production.
- Proficiency in Python and common ML libraries such as TensorFlow, PyTorch, Scikit-learn, and XGBoost.
- Strong understanding of supervised/unsupervised learning, model evaluation, and feature engineering.
Retail & Data Integration:
- Minimum of 2 years working with backend systems or data integration workflows in a commercial or retail setting.
- Experience working with transactional data, product catalogs, customer behavior data, and retail KPIs.
- Familiarity with RESTful API integration and deployment of ML services using Flask, FastAPI, or similar frameworks.
- Proficiency in SQL and working with relational databases (e.g., PostgreSQL, BigQuery) and data warehouses.
Cloud & Infrastructure:
- Hands-on experience with cloud services (GCP preferred).
- Experience with containerization (Docker) and orchestration (Kubernetes) for deploying scalable AI services.
- Familiarity with MLOps tools and workflows (e.g., MLflow, Airflow).
General:
- Strong analytical and problem-solving skills with the ability to translate business problems into technical solutions.
- Comfortable working in Agile teams and collaborating across technical and non-technical functions.
- Strong written and verbal communication skills.
Preferred Qualifications:
- Experience in retail, e-commerce, or home improvement product domains.
- Familiarity with recommendation algorithms (collaborative filtering, content-based filtering).
- Exposure to computer vision (e.g., for product tagging, image search) and NLP (e.g., for intelligent customer support or search optimization).
- Experience with CI/CD pipelines and AI model lifecycle management.
- Experience with real-world ML applications in retail such as recommendation systems, demand forecasting, inventory optimization, or customer segmentation.
- Master s degree in Computer Science, Data Science, Engineering, or a related field.
